AUTOMATED PATTERN RECOGNITION IN REAL-TIME DRILLING DATA FOR EARLY KARST DETECTION
Drilling in carbonate formations often poses a real challenge to operators, contractors and service companies. Severe fluid losses, gas kicks and other unwanted situations increase drilling risks. These risks are closely related to drilling through karsts — vugs, cavities and fractures. Therefore it...
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ftntnutrondheimi:oai:ntnuopen.ntnu.no:11250/2990635 2023-05-15T14:23:12+02:00 AUTOMATED PATTERN RECOGNITION IN REAL-TIME DRILLING DATA FOR EARLY KARST DETECTION Maksimov, Danil Løken, Marius Alexander Pavlov, Alexey Sangesland, Sigbjørn 2021 application/pdf https://hdl.handle.net/11250/2990635 https://doi.org/10.1115/OMAE2021-60529 eng eng ASME ASME 2021 40th International Conference on Ocean, Offshore and Arctic Engineering urn:isbn:978-0-7918-8519-2 https://hdl.handle.net/11250/2990635 https://doi.org/10.1115/OMAE2021-60529 cristin:1986967 Locked until 11.4.2022 due to copyright restrictions. Copyright © 2021 by ASME Chapter 2021 ftntnutrondheimi https://doi.org/10.1115/OMAE2021-60529 2022-04-13T22:39:33Z Drilling in carbonate formations often poses a real challenge to operators, contractors and service companies. Severe fluid losses, gas kicks and other unwanted situations increase drilling risks. These risks are closely related to drilling through karsts — vugs, cavities and fractures. Therefore it is important to detect karsts early enough to avoid drilling into them or, once drilling in a karstification region is detected, to prepare risk mitigating actions. Some geophysical methods can be used for karsts detection, however, they have limitations and cannot guarantee early detection of karsts. One of the recent studies has shown that certain patterns in real-time drilling data can serve as indicators of zones with a higher likelihood of encountering karsts. In this paper, we demonstrate how these patterns can be detected in an automated manner with an adaptive differential filter algorithm. The method has been validated on real drilling data. publishedVersion Book Part Arctic NTNU Open Archive (Norwegian University of Science and Technology) Volume 10: Petroleum Technology |
institution |
Open Polar |
collection |
NTNU Open Archive (Norwegian University of Science and Technology) |
op_collection_id |
ftntnutrondheimi |
language |
English |
description |
Drilling in carbonate formations often poses a real challenge to operators, contractors and service companies. Severe fluid losses, gas kicks and other unwanted situations increase drilling risks. These risks are closely related to drilling through karsts — vugs, cavities and fractures. Therefore it is important to detect karsts early enough to avoid drilling into them or, once drilling in a karstification region is detected, to prepare risk mitigating actions. Some geophysical methods can be used for karsts detection, however, they have limitations and cannot guarantee early detection of karsts. One of the recent studies has shown that certain patterns in real-time drilling data can serve as indicators of zones with a higher likelihood of encountering karsts. In this paper, we demonstrate how these patterns can be detected in an automated manner with an adaptive differential filter algorithm. The method has been validated on real drilling data. publishedVersion |
format |
Book Part |
author |
Maksimov, Danil Løken, Marius Alexander Pavlov, Alexey Sangesland, Sigbjørn |
spellingShingle |
Maksimov, Danil Løken, Marius Alexander Pavlov, Alexey Sangesland, Sigbjørn AUTOMATED PATTERN RECOGNITION IN REAL-TIME DRILLING DATA FOR EARLY KARST DETECTION |
author_facet |
Maksimov, Danil Løken, Marius Alexander Pavlov, Alexey Sangesland, Sigbjørn |
author_sort |
Maksimov, Danil |
title |
AUTOMATED PATTERN RECOGNITION IN REAL-TIME DRILLING DATA FOR EARLY KARST DETECTION |
title_short |
AUTOMATED PATTERN RECOGNITION IN REAL-TIME DRILLING DATA FOR EARLY KARST DETECTION |
title_full |
AUTOMATED PATTERN RECOGNITION IN REAL-TIME DRILLING DATA FOR EARLY KARST DETECTION |
title_fullStr |
AUTOMATED PATTERN RECOGNITION IN REAL-TIME DRILLING DATA FOR EARLY KARST DETECTION |
title_full_unstemmed |
AUTOMATED PATTERN RECOGNITION IN REAL-TIME DRILLING DATA FOR EARLY KARST DETECTION |
title_sort |
automated pattern recognition in real-time drilling data for early karst detection |
publisher |
ASME |
publishDate |
2021 |
url |
https://hdl.handle.net/11250/2990635 https://doi.org/10.1115/OMAE2021-60529 |
genre |
Arctic |
genre_facet |
Arctic |
op_relation |
ASME 2021 40th International Conference on Ocean, Offshore and Arctic Engineering urn:isbn:978-0-7918-8519-2 https://hdl.handle.net/11250/2990635 https://doi.org/10.1115/OMAE2021-60529 cristin:1986967 |
op_rights |
Locked until 11.4.2022 due to copyright restrictions. Copyright © 2021 by ASME |
op_doi |
https://doi.org/10.1115/OMAE2021-60529 |
container_title |
Volume 10: Petroleum Technology |
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1766295746027454464 |